𝗧𝗵𝗲 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗠𝗶𝘀𝘁𝗮𝗸𝗲𝘀 𝗧𝗼 𝗔𝘃𝗼𝗶𝗱 𝗪𝗵𝗲𝗻 𝗔𝗱𝗼𝗽𝘁𝗶𝗻𝗴 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴
You implemented Generative AI Financial Reporting, but your auditors won't accept the AI-generated reports. Why? You didn't update your control environment. Here are five mistakes to avoid:
- Treat AI as a control environment change, not a productivity experiment
- Update your control narratives and define validation procedures
- Establish escalation rules and notify auditors early
- Curate training data thoughtfully, including volume, diversity, and quality
- Establish clear boundaries for AI use and treat AI models like any other system requiring maintenance
Before deploying any Generative AI Financial Reporting tool:
- Update control narratives
- Define sampling procedures
- Establish escalation rules
- Notify auditors early
When training your AI model:
- Use at least 2-3 years of historical reports
- Include various business conditions
- Only train on approved, finalized content
- Label examples that represent best practices
When using AI:
- Drafting narratives where facts are clear is a good use case
- Determining whether an event triggers reassessment is not a good use case
Maintain your AI model:
- Quarterly reviews to assess regulatory changes
- Performance monitoring to detect drift
- Feedback loops to correct mistakes
- Version control to document model versions
Review vendor contracts:
- Ensure explicit prohibitions on using your data for third-party training
- Assess data residency and evaluate access controls
- Plan for exit to retrieve or delete your data
Source: https://dev.to/edith_heroux_aca4c9046ef5/5-critical-mistakes-to-avoid-when-adopting-generative-ai-financial-reporting-59h2 Optional learning community: https://t.me/GyaanSetuAi